Magneto: Unified fine-grained path control in legacy and openflow Hybrid Networks

Cheng Jin, Cristian Lumezanu, Qiang Xu, Hesham Mekky, Zhi-Li Zhang, Guofei Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

28 Scopus citations


Software-defined networking (SDN) provides fine-grained network control and monitoring that simplifies network management. Unfortunately, upgrading existing enterprise networks, comprised of numerous "legacy" switches, to SDN is often cost-prohibitive. We argue that it is possible to achieve most of the benefits of a fully deployed SDN at a fraction of the cost by strategically replacing only few legacy switches with - or introducing a few - new SDN-capable switches in a legacy network, thus creating a hybrid network. We present Magneto, a unified network controller that exerts SDN-like, fine-grained path control over both OpenFlow and legacy switches in hybrid networks. Magneto i) introduces magnet MAC addresses and dynamically updates IPto-magnet MAC mappings at hosts via gratuitous ARP messages for visibility and routing control; and ii) uses the ability of SDN switches to send "custom" packets into the data plane to manipulate legacy switches into updating forwarding entries with magnet MAC addresses for enhanced routing flexibility. Our evaluation on a lab testbed and through extensive simulations on large enterprise network topologies show that Magneto is able to achieve full control over routing when only 20% of network switches are programmable, with negligible computation and latency overhead.

Original languageEnglish (US)
Title of host publicationSOSR 2017 - Proceedings of the 2017 Symposium on SDN Research
PublisherAssociation for Computing Machinery, Inc
Number of pages13
ISBN (Electronic)9781450349475
StatePublished - Apr 3 2017
Event2017 Symposium on SDN Research, SOSR 2017 - Santa Clara, United States
Duration: Apr 3 2017Apr 4 2017

Publication series

NameSOSR 2017 - Proceedings of the 2017 Symposium on SDN Research


Other2017 Symposium on SDN Research, SOSR 2017
Country/TerritoryUnited States
CitySanta Clara

Bibliographical note

Funding Information:
This research was supported in part by NSF grants CNS-1411636, CNS 1618339 and CNS 1617729, DTRA grant HDTRA1-14-1-0040 and DoD ARO MURI Award W911NF-12-1-0385.

Publisher Copyright:
© 2017 ACM.


  • Hybrid SDN
  • MAC learning and forwarding
  • Magneto


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